/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// See docs in ../ops/array_ops.cc.

#define EIGEN_USE_THREADS

#if (defined(GOOGLE_CUDA) && GOOGLE_CUDA) || \
    (defined(TENSORFLOW_USE_ROCM) && TENSORFLOW_USE_ROCM)
#define EIGEN_USE_GPU
#endif

#include "tensorflow/core/kernels/constant_op.h"

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/allocator.h"
#include "tensorflow/core/framework/bounds_check.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/variant_op_registry.h"
#include "tensorflow/core/graph/graph_node_util.h"
#include "tensorflow/core/kernels/fill_functor.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/profiler/lib/scoped_memory_debug_annotation.h"

namespace tensorflow {

namespace {

NodeDef StripTensorDataFromNodeDef(OpKernelConstruction* ctx) {
  const NodeDef& original = ctx->def();
  if (std::is_base_of<protobuf::Message, NodeDef>()) {
    DCHECK_EQ(reinterpret_cast<const protobuf::Message*>(&original)
                  ->GetDescriptor()
                  ->field_count(),
              7)
        << "The NodeDef format has changed, and the attr-stripping code may "
           "need to be updated.";
  }
  NodeDef ret;
  ret.set_name(original.name());
  ret.set_op(original.op());
  ret.set_device(original.device());
  // Strip the "value" attr from the returned NodeDef.
  // NOTE(mrry): The present implementation of `OpKernel::OpKernel()` only uses
  // attrs that affect the cardinality of list-typed inputs and outputs, so it
  // is safe to drop other attrs from the NodeDef.
  AddNodeAttr("dtype", ctx->output_type(0), &ret);
  MergeDebugInfo(original, &ret);
  if (original.has_experimental_type()) {
    *ret.mutable_experimental_type() = original.experimental_type();
  }
  return ret;
}

}  // namespace

ConstantOp::ConstantOp(OpKernelConstruction* ctx)
    : OpKernel(ctx, StripTensorDataFromNodeDef(ctx), false),
      tensor_(ctx->output_type(0)) {
  const TensorProto* proto = nullptr;
  profiler::ScopedMemoryDebugAnnotation op_annotation(name_view().data());
  OP_REQUIRES_OK(ctx, ctx->GetAttr("value", &proto));
  OP_REQUIRES_OK(ctx, ctx->device()->MakeTensorFromProto(
                          *proto, AllocatorAttributes(), &tensor_));
  OP_REQUIRES(
      ctx, ctx->output_type(0) == tensor_.dtype(),
      errors::InvalidArgument("Type mismatch between value (",
                              DataTypeString(tensor_.dtype()), ") and dtype (",
                              DataTypeString(ctx->output_type(0)), ")"));
}

void ConstantOp::Compute(OpKernelContext* ctx) {
  ctx->set_output(0, tensor_);
  if (TF_PREDICT_FALSE(ctx->track_allocations())) {
    ctx->record_persistent_memory_allocation(tensor_.AllocatedBytes());
  }
}

ConstantOp::~ConstantOp() {}

REGISTER_KERNEL_BUILDER(Name("Const").Device(DEVICE_CPU), ConstantOp);
REGISTER_KERNEL_BUILDER(Name("Const").Device(DEVICE_TPU_SYSTEM), ConstantOp);

#if (defined(GOOGLE_CUDA) && GOOGLE_CUDA) || \
    (defined(TENSORFLOW_USE_ROCM) && TENSORFLOW_USE_ROCM)
#define REGISTER_KERNEL(D, TYPE)                                      \
  REGISTER_KERNEL_BUILDER(                                            \
      Name("Const").Device(DEVICE_##D).TypeConstraint<TYPE>("dtype"), \
      ConstantOp);
REGISTER_KERNEL(GPU, Eigen::half);
REGISTER_KERNEL(GPU, bfloat16);
REGISTER_KERNEL(GPU, float);
REGISTER_KERNEL(GPU, double);
REGISTER_KERNEL(GPU, uint8);
REGISTER_KERNEL(GPU, int8);
REGISTER_KERNEL(GPU, qint8);
REGISTER_KERNEL(GPU, uint16);
REGISTER_KERNEL(GPU, int16);
REGISTER_KERNEL(GPU, qint16);
REGISTER_KERNEL(GPU, quint16);
REGISTER_KERNEL(GPU, uint32);
REGISTER_KERNEL(GPU, qint32);
REGISTER_KERNEL(GPU, int64_t);
REGISTER_KERNEL(GPU, uint64);
REGISTER_KERNEL(GPU, complex64);
REGISTER_KERNEL(GPU, complex128);
REGISTER_KERNEL(GPU, bool);
REGISTER_KERNEL(GPU, Variant);
#undef REGISTER_KERNEL
#endif

#define REGISTER_DEFAULT_KERNEL(TYPE)                                     \
  REGISTER_KERNEL_BUILDER(                                                \
      Name("Const").Device(DEVICE_DEFAULT).TypeConstraint<TYPE>("dtype"), \
      ConstantOp);
TF_CALL_NUMBER_TYPES_NO_INT32(REGISTER_DEFAULT_KERNEL);
TF_CALL_QUANTIZED_TYPES(REGISTER_DEFAULT_KERNEL);
TF_CALL_qint16(REGISTER_DEFAULT_KERNEL);
TF_CALL_quint16(REGISTER_DEFAULT_KERNEL);
TF_CALL_bool(REGISTER_DEFAULT_KERNEL);
TF_CALL_variant(REGISTER_DEFAULT_KERNEL);
#undef REGISTER_DEFAULT_KERNEL

typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;

template <typename Device, typename T, typename Index>
class FillOp : public OpKernel {
 public:
  explicit FillOp(OpKernelConstruction* context) : OpKernel(context) {}

  void Compute(OpKernelContext* context) override {
    const Tensor& Tdims = context->input(0);
    OP_REQUIRES(
        context,
        // TODO(rmlarsen): Disallow legacy use of scalars to represent shape.
        (TensorShapeUtils::IsVector(Tdims.shape()) ||
         TensorShapeUtils::IsScalar(Tdims.shape())),
        errors::InvalidArgument("dims must represent a vector, got shape ",
                                Tdims.shape().DebugString()));
    const Tensor& Tvalue = context->input(1);
    OP_REQUIRES(
        context,
        // TODO(rmlarsen): Disallow legacy use of length-1 vector to represent
        // scalar.
        TensorShapeUtils::IsScalar(Tvalue.shape()) ||
            (TensorShapeUtils::IsVector(Tvalue.shape()) &&
             Tvalue.shape().dim_size(0) == 1),
        errors::InvalidArgument("value must represent a scalar, got shape ",
                                Tvalue.shape().DebugString()));
    auto dims = Tdims.flat<Index>();
    TensorShape shape;
    OP_REQUIRES_OK(context, TensorShapeUtils::MakeShape(
                                reinterpret_cast<const Index*>(dims.data()),
                                dims.size(), &shape));
    Tensor* out = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, shape, &out));
    functor::FillFunctor<Device, T> functor;
    functor(context->eigen_device<Device>(), out->flat<T>(),
            Tvalue.scalar<T>());
  }
};

#define REGISTER_KERNEL(D, TYPE)                                     \
  REGISTER_KERNEL_BUILDER(Name("Fill")                               \
                              .Device(DEVICE_##D)                    \
                              .TypeConstraint<TYPE>("T")             \
                              .TypeConstraint<int32>("index_type")   \
                              .HostMemory("dims"),                   \
                          FillOp<D##Device, TYPE, int32>);           \
  REGISTER_KERNEL_BUILDER(Name("Fill")                               \
                              .Device(DEVICE_##D)                    \
                              .TypeConstraint<TYPE>("T")             \
                              .TypeConstraint<int64_t>("index_type") \
                              .HostMemory("dims"),                   \
                          FillOp<D##Device, TYPE, int64>);

#define REGISTER_CPU_KERNEL(TYPE) REGISTER_KERNEL(CPU, TYPE)
TF_CALL_ALL_TYPES(REGISTER_CPU_KERNEL);
// TODO(b/28917570): Add a test for this. Currently python 3 is not happy about
// the conversion from uint8 to quint8.
REGISTER_KERNEL(CPU, quint8);
REGISTER_KERNEL(CPU, quint16);
REGISTER_KERNEL(CPU, qint8);
REGISTER_KERNEL(CPU, qint16);
REGISTER_KERNEL(CPU, qint32);
#undef REGISTER_CPU_KERNEL

#if (defined(GOOGLE_CUDA) && GOOGLE_CUDA) || \
    (defined(TENSORFLOW_USE_ROCM) && TENSORFLOW_USE_ROCM)
REGISTER_KERNEL(GPU, Eigen::half);
REGISTER_KERNEL(GPU, bfloat16);
REGISTER_KERNEL(GPU, float);
REGISTER_KERNEL(GPU, double);
REGISTER_KERNEL(GPU, complex64);
REGISTER_KERNEL(GPU, complex128);
REGISTER_KERNEL(GPU, uint8);
REGISTER_KERNEL(GPU, int8);
REGISTER_KERNEL(GPU, uint16);
REGISTER_KERNEL(GPU, int16);
REGISTER_KERNEL(GPU, int64_t);
REGISTER_KERNEL(GPU, bool);
// Currently we do not support filling strings on GPU
#endif

// A special DEVICE_DEFAULT kernel for int32.
// TODO(b/25387198): Also enable int32 in device memory. This kernel
// registration requires all int32 inputs and outputs to be in host memory.
REGISTER_KERNEL_BUILDER(Name("Fill")
                            .Device(DEVICE_DEFAULT)
                            .TypeConstraint<int32>("T")
                            .TypeConstraint<int32>("index_type")
                            .HostMemory("dims")
                            .HostMemory("value")
                            .HostMemory("output"),
                        FillOp<CPUDevice, int32, int32>);

#undef REGISTER_KERNEL

template <typename Device, typename T>
class ZerosLikeOp : public OpKernel {
 public:
  explicit ZerosLikeOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}

  void Compute(OpKernelContext* ctx) override {
    const Tensor& input = ctx->input(0);
    const Device& d = ctx->eigen_device<Device>();
    if (std::is_same<T, Variant>::value) {
      OP_REQUIRES(
          ctx, input.dims() == 0,
          errors::InvalidArgument("ZerosLike non-scalar Tensor with "
                                  "dtype=DT_VARIANT is not supported."));
      const Variant& v = input.scalar<Variant>()();
      // DT_VARIANT tensors must be allocated on CPU since they wrap C++
      // objects which can not be efficiently represented in GPU memory.
      int numa_node = ctx->device()->NumaNode();
      Tensor out(cpu_allocator(numa_node), DT_VARIANT, TensorShape({}));
      Variant* out_v = &(out.scalar<Variant>()());
      OP_REQUIRES_OK(ctx, UnaryOpVariant<Device>(
                              ctx, ZEROS_LIKE_VARIANT_UNARY_OP, v, out_v));
      ctx->set_output(0, out);
    } else {
      Tensor* out = nullptr;
      OP_REQUIRES_OK(ctx, ctx->forward_input_or_allocate_output(
                              {0}, 0, input.shape(), &out));
      functor::SetZeroFunctor<Device, T> f;
      f(d, out->flat<T>());
    }
  }
};

#define REGISTER_KERNEL(type, dev)                                      \
  REGISTER_KERNEL_BUILDER(                                              \
      Name("ZerosLike").Device(DEVICE_##dev).TypeConstraint<type>("T"), \
      ZerosLikeOp<dev##Device, type>)

#define REGISTER_CPU(type) REGISTER_KERNEL(type, CPU)
TF_CALL_POD_STRING_TYPES(REGISTER_CPU);
REGISTER_CPU(Variant);
#undef REGISTER_CPU

#if (defined(GOOGLE_CUDA) && GOOGLE_CUDA) || \
    (defined(TENSORFLOW_USE_ROCM) && TENSORFLOW_USE_ROCM)
#if !defined(MLIR_GENERATED_GPU_KERNELS_ENABLED)
REGISTER_KERNEL(bool, GPU);
REGISTER_KERNEL(Eigen::half, GPU);
REGISTER_KERNEL(float, GPU);
REGISTER_KERNEL(double, GPU);
REGISTER_KERNEL(int64_t, GPU);
REGISTER_KERNEL(complex64, GPU);
REGISTER_KERNEL(complex128, GPU);
#endif

REGISTER_KERNEL(bfloat16, GPU);
REGISTER_KERNEL(Variant, GPU);
#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
#undef REGISTER_KERNEL

REGISTER_KERNEL_BUILDER(Name("ZerosLike")
                            .Device(DEVICE_DEFAULT)
                            .TypeConstraint<int32>("T")
                            .HostMemory("y"),
                        ZerosLikeOp<CPUDevice, int32>);

template <typename Device, typename T>
class OnesLikeOp : public OpKernel {
 public:
  explicit OnesLikeOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}

  void Compute(OpKernelContext* ctx) override {
    const Tensor& input = ctx->input(0);
    Tensor* out = nullptr;
    OP_REQUIRES_OK(ctx, ctx->forward_input_or_allocate_output(
                            {0}, 0, input.shape(), &out));
    functor::SetOneFunctor<Device, T> f;
    f(ctx->eigen_device<Device>(), out->flat<T>());
  }
};

#define REGISTER_KERNEL(type, dev)                                     \
  REGISTER_KERNEL_BUILDER(                                             \
      Name("OnesLike").Device(DEVICE_##dev).TypeConstraint<type>("T"), \
      OnesLikeOp<dev##Device, type>)

#define REGISTER_CPU(type) REGISTER_KERNEL(type, CPU)
TF_CALL_POD_TYPES(REGISTER_CPU);
#undef REGISTER_CPU

#if (defined(GOOGLE_CUDA) && GOOGLE_CUDA) || \
    (defined(TENSORFLOW_USE_ROCM) && TENSORFLOW_USE_ROCM)
#if !defined(MLIR_GENERATED_GPU_KERNELS_ENABLED)
REGISTER_KERNEL(bool, GPU);
REGISTER_KERNEL(Eigen::half, GPU);
REGISTER_KERNEL(float, GPU);
REGISTER_KERNEL(double, GPU);
REGISTER_KERNEL(int64_t, GPU);
REGISTER_KERNEL(complex64, GPU);
REGISTER_KERNEL(complex128, GPU);
#endif
REGISTER_KERNEL(bfloat16, GPU);
#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM

#undef REGISTER_KERNEL

REGISTER_KERNEL_BUILDER(Name("OnesLike")
                            .Device(DEVICE_DEFAULT)
                            .TypeConstraint<int32>("T")
                            .HostMemory("y"),
                        OnesLikeOp<CPUDevice, int32>);

PlaceholderOp::PlaceholderOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
  OP_REQUIRES_OK(ctx, ctx->GetAttr("shape", &expected_shape_));
}

void PlaceholderOp::Compute(OpKernelContext* ctx) {
  if (expected_shape_.dims() > 0) {
    OP_REQUIRES(ctx, false,
                errors::InvalidArgument(
                    "You must feed a value for placeholder tensor '", name(),
                    "' with dtype ", DataTypeString(output_type(0)),
                    " and shape ", expected_shape_.DebugString()));
  } else {
    OP_REQUIRES(ctx, false,
                errors::InvalidArgument(
                    "You must feed a value for placeholder tensor '", name(),
                    "' with dtype ", DataTypeString(output_type(0))));
  }
}

REGISTER_KERNEL_BUILDER(Name("Placeholder").Device(DEVICE_CPU), PlaceholderOp);
REGISTER_KERNEL_BUILDER(Name("PlaceholderV2").Device(DEVICE_CPU),
                        PlaceholderOp);
// The following GPU/Default kernel registration is used to address the
// situation that a placeholder is added in a GPU device context and soft
// placement is false. Since a placeholder should never be executed, adding
// these GPU kernels has no effect on graph execution.
REGISTER_KERNEL_BUILDER(Name("Placeholder").Device(DEVICE_DEFAULT),
                        PlaceholderOp);
REGISTER_KERNEL_BUILDER(Name("PlaceholderV2").Device(DEVICE_DEFAULT),
                        PlaceholderOp);
}  // namespace tensorflow
